Downloads · 30 days
12
2% of all-time downloads
wavelm-study
0xmagical/wavelm-study
wavelm-study is a audio classification model from 0xmagical. Use it for the audio classification task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
644
Public
Parameters
94.6M
6.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors378 MB · 100%
At a glance
- Task
- Audio Classification
- Library
- transformers
- Model type
- wavlm
- Access
- Public
- Created
- Jan 22, 2024
- Updated
- Jan 27, 2024
- SHA
- 5d9f97e9
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
wavelm-study
This model is a fine-tuned version of microsoft/wavlm-base-plus on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5855
- Accuracy: 0.8691
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3109 | 1.0 | 7220 | 0.5189 | 0.8643 |
| 0.2798 | 2.0 | 14440 | 0.5241 | 0.8661 |
| 0.2721 | 3.0 | 21660 | 0.5855 | 0.8691 |
Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Audio Classification
- Library
- transformers
- Type
- wavlm
- Created
- Jan 22, 2024
- Updated
- Jan 27, 2024